18 citations · 44 across the 24 of their papers we have counts for
25 papers · 1 filter
Next Forcing: Causal World Modeling with Multi-Chunk Prediction
Gangwei Xu, Qihang Zhang, Jiaming Zhou +4
Autoregressive video generation has emerged as a powerful paradigm for World Action Models (WAMs). However, existing approaches suffer from slow training convergence and limited co…
PointForward: Feedforward Driving Reconstruction through Point-Aligned Representations
Cheng Chi, Xianqi Wang, Hongcheng Luo +9
High-fidelity reconstruction of driving scenes is crucial for autonomous driving. While recent feedforward 3D Gaussian Splatting (3DGS) methods enable fast reconstruction, their pe…
PCSTracker: Long-Term Scene Flow Estimation for Point Cloud Sequences
Min Lin, Gangwei Xu, Xianqi Wang +2
Point cloud scene flow estimation is fundamental to long-term and fine-grained 3D motion analysis. However, existing methods are typically limited to pairwise settings and struggle…
PromptStereo: Zero-Shot Stereo Matching via Structure and Motion Prompts
Xianqi Wang, Hao Yang, Hangtian Wang +4
Modern stereo matching methods have leveraged monocular depth foundation models to achieve superior zero-shot generalization performance. However, most existing methods primarily f…
Pixel-Perfect Visual Geometry Estimation
Gangwei Xu, Haotong Lin, Hongcheng Luo +6
Recovering clean and accurate geometry from images is essential for robotics and augmented reality. However, existing geometry foundation models still suffer severely from flying p…
Generalized Geometry Encoding Volume for Real-time Stereo Matching
Jiaxin Liu, Gangwei Xu, Xianqi Wang +2
Real-time stereo matching methods primarily focus on enhancing in-domain performance but often overlook the critical importance of generalization in real-world applications. In con…
Pixel-Perfect Depth with Semantics-Prompted Diffusion Transformers
Gangwei Xu, Haotong Lin, Hongcheng Luo +11
This paper presents Pixel-Perfect Depth, a monocular depth estimation model based on pixel-space diffusion generation that produces high-quality, flying-pixel-free point clouds fro…
BANet: Bilateral Aggregation Network for Mobile Stereo Matching
Gangwei Xu, Jiaxin Liu, Xianqi Wang +5
State-of-the-art stereo matching methods typically use costly 3D convolutions to aggregate a full cost volume, but their computational demands make mobile deployment challenging. D…
BAT: Learning Event-based Optical Flow with Bidirectional Adaptive Temporal Correlation
Gangwei Xu, Haotong Lin, Zhaoxing Zhang +3
Event cameras deliver visual information characterized by a high dynamic range and high temporal resolution, offering significant advantages in estimating optical flow for complex…
ZeroStereo: Zero-shot Stereo Matching from Single Images
Xianqi Wang, Hao Yang, Gangwei Xu +6
State-of-the-art supervised stereo matching methods have achieved remarkable performance on various benchmarks. However, their generalization to real-world scenarios remains challe…
MonSter++: Unified Stereo Matching, Multi-view Stereo, and Real-time Stereo with Monodepth Priors
Junda Cheng, Wenjing Liao, Zhipeng Cai +10
We introduce MonSter++, a geometric foundation model for multi-view depth estimation, unifying rectified stereo matching and unrectified multi-view stereo. Both tasks fundamentally…
FlowMamba: Learning Point Cloud Scene Flow with Global Motion Propagation
Min Lin, Gangwei Xu, Yun Wang +2
Scene flow methods based on deep learning have achieved impressive performance. However, current top-performing methods still struggle with ill-posed regions, such as extensive fla…
Event-assisted 12-stop HDR Imaging of Dynamic Scene
Shi Guo, Zixuan Chen, Ziran Zhang +3
High dynamic range (HDR) imaging is a crucial task in computational photography, which captures details across diverse lighting conditions. Traditional HDR fusion methods face limi…
Leveraging Consistent Spatio-Temporal Correspondence for Robust Visual Odometry
Zhaoxing Zhang, Junda Cheng, Gangwei Xu +3
Recent approaches to VO have significantly improved performance by using deep networks to predict optical flow between video frames. However, existing methods still suffer from noi…
Hybrid Cost Volume for Memory-Efficient Optical Flow
Yang Zhao, Gangwei Xu, Gang Wu
Current state-of-the-art flow methods are mostly based on dense all-pairs cost volumes. However, as image resolution increases, the computational and spatial complexity of construc…
IGEV++: Iterative Multi-range Geometry Encoding Volumes for Stereo Matching
Gangwei Xu, Xianqi Wang, Zhaoxing Zhang +3
Stereo matching is a core component in many computer vision and robotics systems. Despite significant advances over the last decade, handling matching ambiguities in ill-posed regi…
HDRFlow: Real-Time HDR Video Reconstruction with Large Motions
Gangwei Xu, Yujin Wang, Jinwei Gu +2
Reconstructing High Dynamic Range (HDR) video from image sequences captured with alternating exposures is challenging, especially in the presence of large camera or object motion.…
Selective-Stereo: Adaptive Frequency Information Selection for Stereo Matching
Xianqi Wang, Gangwei Xu, Hao Jia +1
Stereo matching methods based on iterative optimization, like RAFT-Stereo and IGEV-Stereo, have evolved into a cornerstone in the field of stereo matching. However, these methods s…
FlowDA: Unsupervised Domain Adaptive Framework for Optical Flow Estimation
Miaojie Feng, Longliang Liu, Hao Jia +2
Collecting real-world optical flow datasets is a formidable challenge due to the high cost of labeling. A shortage of datasets significantly constrains the real-world performance o…
Memory-Efficient Optical Flow via Radius-Distribution Orthogonal Cost Volume
Gangwei Xu, Shujun Chen, Hao Jia +2
The full 4D cost volume in Recurrent All-Pairs Field Transforms (RAFT) or global matching by Transformer achieves impressive performance for optical flow estimation. However, their…
MC-Stereo: Multi-peak Lookup and Cascade Search Range for Stereo Matching
Miaojie Feng, Junda Cheng, Hao Jia +4
Stereo matching is a fundamental task in scene comprehension. In recent years, the method based on iterative optimization has shown promise in stereo matching. However, the current…
Iterative Geometry Encoding Volume for Stereo Matching
Gangwei Xu, Xianqi Wang, Xiaohuan Ding +1
Recurrent All-Pairs Field Transforms (RAFT) has shown great potentials in matching tasks. However, all-pairs correlations lack non-local geometry knowledge and have difficulties ta…
CGI-Stereo: Accurate and Real-Time Stereo Matching via Context and Geometry Interaction
Gangwei Xu, Huan Zhou, Xin Yang
In this paper, we propose CGI-Stereo, a novel neural network architecture that can concurrently achieve real-time performance, competitive accuracy, and strong generalization abili…
Accurate and Efficient Stereo Matching via Attention Concatenation Volume
Gangwei Xu, Yun Wang, Junda Cheng +2
Stereo matching is a fundamental building block for many vision and robotics applications. An informative and concise cost volume representation is vital for stereo matching of hig…